# MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution

arXiv: https://arxiv.org/abs/2412.04861 · HTML: https://mlsanity.com/paper/2412.04861/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Physiological signal modeling](https://mlsanity.com/ssm/physiological-signal-modeling/)

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [An Investigation of Incorporating Mamba for Speech Enhancement](https://mlsanity.com/paper/2405.06573/) (2024)
- [DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal](https://mlsanity.com/paper/2208.00542/) (2022)
- [Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution](https://mlsanity.com/paper/2210.15793/) (2022)
- [Super-Resolution using Convolutional Neural Networks without Any Checkerboard Artifacts](https://mlsanity.com/paper/1806.02658/) (2018)
- [Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey](https://mlsanity.com/paper/2404.16223/) (2024)
- [TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement](https://mlsanity.com/paper/2410.03843/) (2024)
- [SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification](https://mlsanity.com/paper/2012.03803/) (2020)
- [IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis](https://mlsanity.com/paper/2312.09445/) (2023)
- [Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs](https://mlsanity.com/paper/2210.15887/) (2022)
- [DCAE-SR: Design of a Denoising Convolutional Autoencoder for reconstructing Electrocardiograms signals at Super Resolution](https://mlsanity.com/paper/2404.15307/) (2024)

## Built on by (most cited first, 2 of 2)

- [S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG](https://mlsanity.com/paper/2509.03066/) (2025)
- [Robust Photoplethysmography Signal Denoising via Mamba Networks](https://mlsanity.com/paper/2510.11058/) (2025)

## Abstract (arXiv)

Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monitoring, super-resolution (SR) techniques have been developed, enabling these devices to collect and transmit signals at a lower sampling rate. In this study, we propose MSECG, a compact neural network model designed for ECG SR. MSECG combines the strength of the recurrent Mamba model with convolutional layers to capture both local and global dependencies in ECG waveforms, allowing for the effective reconstruction of high-resolution signals. We also assess the model's performance in real-world noisy conditions by utilizing ECG data from the PTB-XL database and noise data from the MIT-BIH Noise Stress Test Database. Experimental results show that MSECG outperforms two contemporary ECG SR models under both clean and noisy conditions while using fewer parameters, offering a more powerful and robust solution for long-term ECG monitoring applications.